main.py

from wsgiref import simple_server
from flask import Flask, request, render_template
from flask import Response
import flask_monitoringdashboard as dashboard
import pandas as pd
import os
from flask_cors import CORS, cross_origin
from apps.training.train_model import TrainModel
from apps.prediction.predict_model import PredictModel
from apps.core.config import Config

app = Flask(__name__)
dashboard.bind(app)
CORS(app)

@app.route('/', methods=['POST','GET'])
def index_page():
    """
    * method: index_page
    * description: method to call index html page
    * return: index.html
    *
    * who             when           version  change (include bug# if apply)
    * ----------      -----------    -------  ------------------------------
    * shubhankar      05-JULY-2023    1.0      initial creation
    *
    * Parameters
    *   None
    """

    return render_template('index.html')



@app.route('/training', methods=['POST'])
@cross_origin()
def training_route_client():
    """
    * method: training_route_client
    * description: method to call training route
    * return: none
    *
    * who             when           version  change (include bug# if apply)
    * ----------      -----------    -------  ------------------------------
    * shubhankar       05-JULY-2023    1.0      initial creation
    *
    * Parameters
    *   None
    """
    try:
        config = Config()
        #get run id
        run_id = config.get_run_id()
        data_path = config.training_data_path
        #trainmodel object initialization
        trainModel=TrainModel(run_id,data_path)
        #training the model
        trainModel.training_model()
        return Response("Training successfull! and its RunID is : "+str(run_id))
    except ValueError:
        return Response("Error Occurred! %s" % ValueError)
    except KeyError:
        return Response("Error Occurred! %s" % KeyError)
    except Exception as e:
        return Response("Error Occurred! %s" % e)

@app.route('/batchprediction', methods=['POST'])
@cross_origin()
def batch_prediction_route_client():
    """
    * method: batch_prediction_route_client
    * description: method to call batch prediction route
    * return: none
    *
    * who             when           version  change (include bug# if apply)
    * ----------      -----------    -------  ------------------------------
    * shubhankar       05-JULY-2023    1.0      initial creation
    *
    * Parameters
    *   None
    """
    try:
        config = Config()
        #get run id
        run_id = config.get_run_id()
        data_path = config.prediction_data_path
        #prediction object initialization
        predictModel=PredictModel(run_id, data_path)
        #prediction the model
        predictModel.batch_predict_from_model()
        return Response("Prediction successfull! and its RunID is : "+str(run_id))
    except ValueError:
        return Response("Error Occurred! %s" % ValueError)
    except KeyError:
        return Response("Error Occurred! %s" % KeyError)
    except Exception as e:
        return Response("Error Occurred! %s" % e)


@app.route('/prediction', methods=['POST'])
@cross_origin()
def single_prediction_route_client():
    """
    * method: prediction_route_client
    * description: method to call prediction route
    * return: none
    *
    * who             when           version  change (include bug# if apply)
    * ----------      -----------    -------  ------------------------------
    * shubhankar       05-JULY-2023    1.0      initial creation
    *
    * Parameters
    *   None
    """
    try:
        config = Config()
        #get run id
        run_id = config.get_run_id()
        data_path = config.prediction_data_path
        print('Test')

        if request.method == 'POST':
            high_bp = request.form['high_bp']
            high_chol = request.form['high_chol']
            chol_check = request.form['chol_check']
            bmi = request.form['bmi']
            smoker = request.form['smoker']
            stroke = request.form['stroke']
            heart_disease_or_attack = request.form['heart_disease_or_attack']
            phys_activity = request.form['phys_activity']
            fruits = request.form['fruits']
            veggies = request.form['veggies']
            hvy_alcohol_consump = request.form['hvy_alcohol_consump']
            any_health_care = request.form['any_health_care']
            no_doc_bc_cost = request.form['no_doc_bc_cost']
            gen_hlth = request.form['gen_hlth']
            ment_hlth = request.form['ment_hlth']
            phys_hlth = request.form['phys_hlth']
            diff_walk = request.form['diff_walk']
            sex = request.form['sex']
            age = request.form['age']
            education = request.form['education']
            income = request.form['income']

            data = pd.DataFrame(data=[[0, high_bp, high_chol,chol_check,bmi,smoker,stroke,heart_disease_or_attack,phys_activity,fruits,veggies,hvy_alcohol_consump,any_health_care,no_doc_bc_cost,gen_hlth,ment_hlth,phys_hlth,diff_walk,sex,age,education,income]],
                              columns=['id','high_bp','high_chol','chol_check','bmi','smoker','stroke','heart_disease_or_attack','phys_activity','fruits','veggies','hvy_alcohol_consump','any_health_care','no_doc_bc_cost','gen_hlth','ment_hlth','phys_hlth','diff_walk','sex','age','education','income'])
            # using dictionary to convert specific columns
            convert_dict = {"id" : int,
                            "high_bp" : float,
                            "high_chol" : float,
                            "chol_check" : float,
                            "bmi" : float,
                            "smoker" : float,
                            "stroke" : float,
                            "heart_disease_or_attack" : float,
                            "phys_activity" : float,
                            "fruits" : float,
                            "veggies" : float,
                            "hvy_alcohol_consump" : float,
                            "any_health_care" : float,
                            "no_doc_bc_cost" : float,
                            "gen_hlth" : float,
                            "ment_hlth" : float,
                            "phys_hlth" : float,
                            "diff_walk" : float,
                            "sex" : float,
                            "age" : float,
                            "education" : float,
                            "income" : float}

            data = data.astype(convert_dict)

            # object initialization
            predictModel = PredictModel(run_id, data_path)
            # prediction the model
            output = predictModel.single_predict_from_model(data)
            print('output : '+str(output))
            return Response("Predicted Output is : "+str(output))
    except ValueError:
        return Response("Error Occurred! %s" % ValueError)
    except KeyError:
        return Response("Error Occurred! %s" % KeyError)
    except Exception as e:
        return Response("Error Occurred! %s" % e)

if __name__ == "__main__":
    #app.run()
    host = '0.0.0.0'
    port = 5000
    httpd = simple_server.make_server(host, port, app)
    httpd.serve_forever()